RESOURCE ALLOCATION IN COORDINATED MULTIPOINT LONG

RESOURCE ALLOCATION IN COORDINATED MULTIPOINT LONG TERM

EVOLUTION –ADVANCED NETWORKS

NORSHIDAH BINTI KATIRAN

A thesis submitted in fulfilment of the

requirements for the award of the degree of

Doctor of Philosophy (Electrical Engineering)

Faculty of Electrical Engineering

Universiti Teknologi Malaysia

JULY 2015

v

ABSTRACT

Coordinated Multipoint (CoMP) in Long Term Evolution-Advanced (LTE-

Advanced) improves the cell-edge data rates and the network spectral efficiency

through base station coordination. In order to achieve high quality of service (QoS)

in CoMP network, resource allocation approach is one of the main challenges. The

resource allocation strategies of cells in CoMP network affect each other’s

performance. Thus, the resource allocation approach should consider various

diversities offered in multiuser wireless networks, particularly in frequency, spatial

and time dimensions. The primary objective of this research is to develop resource

allocation strategy for CoMP network that can provide high QoS. The resource

allocation algorithm is developed through three phases, namely Low-Complexity

Resource Allocation (LRA), Optimized Resource Allocation (ORA) and Cross-Layer

Design of ORA (CLD-ORA). The LRA algorithm is a three-step resource allocation

scheme that consists of user selection module, subcarrier allocation module and

power allocation module which are performed sequentially in a multi-antenna CoMP

network. The proposed ORA algorithm enhances throughput in LRA while ensuring

fairness. ORA is formulated based on Lagrangian method and optimized using

Particle Swarm Optimization (PSO). The design of CLD-ORA algorithm is an

enhancement of the ORA algorithm with resource block (RB) scheduling scheme at

medium access control (MAC) layer. Simulation study shows that the ORA

algorithm improves the network sum-rate and fairness index up to 70% and 25%,

respectively and reduces the average transmit power by 41% in relative to LRA

algorithm. The CLD-ORA algorithm has further enhanced the LRA and ORA

algorithms with network sum-rate improvement of 77% and 33%, respectively. The

proposed resource allocation algorithm has been proven to provide a significant

improved performance for CoMP LTE-Advanced network and can be extended to

future 5G network.

vi

ABSTRAK

Pengkoordinatan Berbilang Punca (CoMP) dalam Evolusi Jangka Panjang-

Termaju (LTE-Advanced) meningkatkan kadar data dan keberkesanan spektrum

rangkaian melalui pengkoordinatan stesen pangkalan (BS). Bagi mencapai kualiti

perkhidmatan (QoS) yang tinggi dalam rangkaian CoMP, pendekatan pengagihan

sumber menjadi satu cabaran utama. Strategi pengagihan sumber oleh sel-sel dalam

rangkaian CoMP memberi kesan terhadap prestasi setiap sel. Oleh itu, pendekatan

pengagihan sumber perlu mempertimbang kepelbagaian dalam rangkaian tanpa

wayar berbilang pengguna, terutama dalam dimensi frekuensi, ruang dan masa.

Objektif utama kajian ini ialah membangunkan strategi pengagihan sumber bagi

rangkaian CoMP yang memberikan QoS yang tinggi. Algoritma pengagihan sumber

ini dibangunkan melalui tiga fasa, iaitu Pengagihan Kuasa Kekompleksan Rendah

(LRA), Pengagihan Kuasa Teroptimum (ORA) dan Rekabentuk Silang Lapisan ORA

(CLD-ORA). Algoritma LRA ialah kaedah pengagihan kuasa tiga-langkah terdiri

daripada modul pemilihan pengguna, modul pengagihan subpembawa dan modul

pengagihan kuasa yang dijalankan secara berturutan dalam rangkaian CoMP

berbilang antena. Algoritma ORA yang dicadangkan meningkatkan daya

pemprosesan LRA di samping memastikan keadilan. ORA diformulasi berdasarkan

kaedah Lagrangian dan dioptimum menggunakan Pengoptimuman Kerumunan Zarah

(PSO). Rekabentuk CLD-ORA adalah penambahbaikan ORA dengan kaedah

penjadualan blok sumber (RB) di lapisan kawalan capaian medium (MAC). Kajian

simulasi menunjukkan ORA meningkatkan hasil tambah kadar rangkaian dan

keadilan sehingga 70% dan 25%, dan mengurangkan kuasa pancaran purata sehingga

41% berbanding LRA. CLD-ORA menambahbaik LRA dan ORA dengan

peningkatan hasil tambah kadar 77% dan 33%. Algoritma pengagihan sumber yang

dicadangkan terbukti meningkatkan prestasi rangkaian CoMP LTE-Advanced dan

boleh dipanjangkan kepada rangkaian 5G masa hadapan.

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TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENTS iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES xi

LIST OF FIGURES xii

LIST OF ABBREVIATIONS xv

LIST OF SYMBOLS xvii

LIST OF APPENDIX xx

1 INTRODUCTION 1

1.1 Overview 1

1.2 Problem Statement 2

1.3 Research Objectives 3

1.4 Scope of Research 4

1.5 Significance of Research 5

1.6 Research Contributions 5

1.7 Thesis Organization 7

2 BACKGROUND AND RELATED WORKS 9

2.1 Overview 9

2.2 Coordinated Multipoint (CoMP) in LTE-Advanced 10

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2.2.1 LTE-Advanced 10

2.2.2 CoMP Transmission 12

2.2.3 Multicarrier Modulation and Multiple Access 17

2.2.4 Downlink Radio Resources 19

2.3 Challenges of CoMP 21

2.3.1 Clustering 21

2.3.2 Synchronization 22

2.3.3 Channel Knowledge 23

2.3.4 Backhaul 23

2.4 Optimization Methods 24

2.4.1 Swarm Intelligence 24

2.4.2 Particle Swarm Optimization 25

2.5 Related Works 26

2.5.1 Low-Complexity Resource Allocation 28

2.5.2 Optimized Resource Allocation 30

2.5.3 Cross-Layer Design 32

2.6 Chapter Summary 34

3 RESOURCE ALLOCATION DESIGN APPROACH

IN COORDINATED MULTIPOINT (CoMP)

LTE-ADVANCED NETWORK 36

3.1 Overview 36

3.2 Proposed Resource Allocation Algorithm 37

3.2.1 Low-Complexity Resource Allocation (LRA) 40

3.2.2 Optimized Resource Allocation (ORA) 41

3.2.3 Cross-Layer Design of Optimized Resource

Allocation (CLD-ORA) 42

3.3 Network Model 43

3.3.1Multiuser Downlink Physical Layer Model 45

3.4 Performance Metrics 47

3.4.1 Network Sum-Rate 47

3.4.2 Average Transmit Power 48

3.4.3 Fairness Index 49

3.4.4 Network Spectrum Efficiency 49

ix

3.4.5 Network Energy Efficiency 50

3.5 Simulation Model 50


4 LOW-COMPLEXITY RESOURCE ALLOCATION

FOR COORDINATED MULTIPOINT (CoMP)


4.1 Overview 54

4.2 Problem Formulation 55

4.3 LRA Design Approach 57

4.3.1 User Selection Module 58

4.3.2 Subcarrier Allocation Module 59

4.3.3 Power Allocation Module 61

4.4 Simulation Scenario 64

4.5 Simulation Study of LRA 65




4.5.4 Network Spectral Efficiency 73



5 OPTIMIZED RESOURCE ALLOCATION

FOR COORDINATED MULTIPOINT (CoMP)


5.1 Overview 76

5.2 Problem Formulation 77

5.3 Lagrangian Method 78

5.3.1 Karush-Kuhn Tucker (KKT) Theorem 80

5.3.2 Application of Lagrange and KKT theorem in

Network Proportional Fairness

Utility Maximization 81

5.4 Optimization Tools 82

5.4.1 Subgradient Method 82

x

5.4.2 Particle Swarm Optimization (PSO) 84

5.5 ORA Design Approach 87


5.7 Simulation Study of ORA 89

5.7.1 PSO Performance 90




5.7.5 Network Spectral Efficiency 103



6 CROSS-LAYER DESIGN OPTIMIZED RESOURCE

ALLOCATION FOR COORDINATED MULTIPOINT

(CoMP) LTE-ADVANCED NETWORK 106

6.1 Overview 106

6.2 Proposed CLD for CoMP LTE-Advanced 107

6.2.1 CLD-ORA Design Approach 109


6.4 Simulation Study of CLD-ORA 115

6.4.1 Network Throughput 115



7 CONCLUSION AND FUTURE WORKS 123

7.1 Overview 123

7.2 Significant Achievement 124

7.3 Future Works 127

REFERENCES 129

Appendices A-B 143-157

xi

LIST OF TABLES

TABLE NO. TITLE PAGE

2.1 LTE-Advanced system attributes 10

2.2 Summary of related low-complexity algorithm 30

2.3 Summary of related optimized algorithm 31

2.4 Summary of related MAC scheduling scheme 34

3.1 Parameters of CoMP LTE-Advanced network 45

4.1 User positions with respect to the cell center 66

4.2 Throughput achieved by individual user 68

5.1 PSO parameter settings 90

5.2 The performance of PSO and subgradient method for ORA 91

5.3 Convergence points and sum-rate results for different cell loading 94

5.4 User positions with respect to the cell center 95

5.5 Throughput achieved by each cell-edge user 97

6.1 User distances from the serving BS 116

6.2 Throughput achieved by each cell-edge user 118

xii

LIST OF FIGURES

FIGURE NO. TITLE PAGE

2.1 The key enabling technologies in LTE-Advanced 11

2.2 An MIMO system 12

2.3 Downlink CoMP (JT) transmission 14

2.4 Downlink CoMP (DCS) transmission 16

2.5 Downlink CoMP (CS/CB) transmission 17

2.6 Spectral characteristic of OFDM transmission scheme [2] 18

2.7 LTE downlink frame structure 20

2.8 OFDMA reference symbols to support MIMO transmission 21

2.9 Physical meanings of the parameters, functions and

constraints in resource allocation for wireless networks 27

3.1 Block diagram of the proposed resource allocation

algorithm 37

3.2 Decision of resource allocation strategy 39

3.3 Flow chart of LRA 40

3.4 Flow chart of ORA 41

3.5 Flow chart of CLD-ORA 42

3.6 Downlink CoMP LTE-Advanced network model 43

3.7 Metrics used for performance evaluation 47

3.8 Simulation model 52

3.9 Achievable spectral efficiency versus SNR

4.1 LRA framework for CoMP LTE-Advanced 57

4.2 Block diagram of the user selection module in LRA 59

4.3 Block type of subcarrier allocation 60

4.4 Algorithm for subcarrier allocation module in LRA 60

4.5 Power allocation scheme according to WF algorithm 62

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4.6 Algorithm for power allocation module in LRA 62

4.7 LRA flowchart 63

4.8 Simulation model 64

4.9 Sum-rate achieved by LRA and OPO algorithms 66

4.10 Cell-edge performance with , and

67

4.11 Network sum-rate for LRA with various

configurations 69

4.12 Average transmit power 70

4.13 Average transmit power with various configurations 70

4.14 Fairness index versus cell load 72

4.15 Fairness index achieved by each cell 72

4.16 Spectrum efficiency achieved by the network with LRA and

OPO 73

4.17 Network energy efficiency versus cell load 74

5.1 ORA flowchart 85

5.2 Solving ORA using PSO algorithm 86

5.3 ORA framework in downlink CoMP system 88

5.4 Multiuser CoMP network topology 89

5.5 Performance comparison of CPU execution time by using

PSO and subgradient method 92

5.6 Network sum-rate for the different algorithms versus the

number of iterations for , and 94

5.7 Sum-rate achieved by the different algorithm 95

5.8 Cell-edge achievable rate comparison with ,

and 96

5.9 Network sum-rate for ORA algorithm versus cell load with various

configurations 98

5.10 Average BS transmit power versus the number of iterations 99

5.11 Average BS transmit power versus cell load 100

5.12 Average transmit power per BS versus cell load with various

configurations 100

5.13 JFI versus cell load 102

xiv

5.14 Cell-edge JFI obtained by each cell with ,

and 102

5.15 Spectrum efficiency achieved by the network with LRA,

SRM, ORA and WSRM 103

5.16 Network energy efficiency achieved by the network with

LRA, SRM, ORA and WSRM 104

6.1 General cross-layer architecture for LTE-Advanced

networks 108

6.2 Proposed CLD-ORA framework 109

6.3 Detail framework of CLD-ORA 110

6.4 PFS algorithm 111

6.5 Overview of the proposed CLD-ORA operation 112

6.6 CLD-ORA flowchart 113

6.7 LTE physical resource structure 114

6.8 Network throughput comparison of PFS and RR schemes

implemented in CLD-ORA 116

6.9 Cell-edge throughput achieved by individual cell 117

6.10 Throughput comparison for LRA, SRM, ORA and CLD-ORA

with different cell load 119

6.11 JFI comparison of CLD-ORA with PFS and RR schemes 120

6.12 Cell-edge JFI comparison of CLD-ORA with PFS and

RR schemes 121

6.13 JFI comparison of LRA, ORA and CLD-ORA 121

xv

LIST OF ABBREVIATIONS

APP - Application layer

BS - Base Station

CEU - Cell-edge User

CLD - Cross-Layer Design

CLD-ORA - Cross-Layer Design of Optimized Resource Allocation

CoMP - Coordinated Multipoint

CS/CB - Coordinated Scheduling/Coordinated Beamforming

CSI - Channel State Information

DCS - Dynamic Cell Selection

GA - Genetic Algorithm

ICI - Inter-Cell Interference

ICIC - Inter-Cell Interference Coordination

JFI - Jain’s Fairness Index

JP - Joint Processing

JT - Joint Transmission

KKT - Karush-Kuhn-Tucker

LRA - Low-Complexity Resource Allocation

LTE - Long Term Evolution

LTE-A - Long Term Evolution – Advanced

MAC - Medium Access Control

MIMO - Multiple-Input Multiple-Output

MU-MIMO - Multi-User MIMO

NEE - Network Energy Efficiency

NSR - Noise-to-Signal Ratio

OAM - Operation and Management

OFDM - Orthogonal Frequency Division Multiplexing

xvi

OFDMA - Orthogonal Frequency Division Multiple Access

OPO - Orthogonal Projection Operator

ORA - Optimized Resource Allocation

PDCP - Packet Data Control Protocol

PF - Proportional Fairness

PFS - Proportional Fair Scheduling

PHY - Physical layer

PRB - Physical Resource Block

PSO - Particle Swarm Optimization

QoS - Quality of Service

RB - Resource Block

RF - Radio Frequency

RLC - Radio Link Control

RR - Round-Robin

RRM - Radio Resource Management

SC - Selective Combining

SE - Spectral Efficiency

SNR - Signal-to-Noise Ratio

SINR - Signal-to-Interference plus Noise Ratio

SISO - Single-Input Single Output

SRM - Sum-Rate Maximization

SVD - Singular Value Decomposition

TTI - Transmission Time Interval

UE - User End

UMTS - Universal Mobile Terrestrial System

WF - Water-Filling

WSRM - Weighted Sum-Rate Maximization

3GPP - The Third Generation Partnership Project

xvii

LIST OF SYMBOLS

- selected user set

- network bandwidth

- cell

- acceleration coefficients

- number of swarm particles

- signal transmitted by cell

, - reference signals for four antenna ports MIMO transmission

- fitness

- global best position

- channel matrix of UE over subcarrier

- channel matrix of UE in cell over subcarrier

- interference caused by UE in cell over subcarrier

- interference received over subcarrier in cell

- total number of BSs in the CoMP network

- fairness index achieved by cell-edge UEs

- total number of UEs in the CoMP network

- total number of UE in cell

- total number of UE in cell

- corresponding UE in cell

- corresponding UE in cell

- number of other cells in the CoMP network

- path loss between BS and UE

- maximum number of iteration

- total number of subcarriers in the system

- noise power

xviii

- total number of receive antennas

- number of subcarriers fixed for each user in LRA

- total number of transmit antennas

- corresponding subcarrier of UE

- number of transmit antennas at UE device

- number of transmit antennas at the BS

- white noise at the receiver in cell

- personal best position of particle

- maximum BS transmit power

- average transmit power

- total power allocated for user over the set of subcarriers

- power allocated to UE in cell over subcarrier

- optimal power allocated to UE in cell over subcarrier

- power allocated to UE in cell over subcarrier

,

- random numbers uniformly distributed on (0,1)

- total achievable rate of cell-edge UEs in cell

- achievable rate of UE in cell over subcarrier

- achievable rate of UE

- minimum rate requirement of UE

- total achieved rate in a previous time window of fixed duration

- achievable rate for the -th user over -th RB at time TTI

- rank of

- unitary matrix of UE in cell over subcarrier

- right singular vector of on spatial layer

- vector matrix of UE in cell over subcarrier

- left singular vector of on spatial layer

- inertia weight

- precoding matrix at cell

- final weight

- initial weight

xix

- current position of particle

- Lagrange multiplier

- subcarrier allocation indicator

- singular matrix of user in cell over subcarrier

- singular value of on spatial layer

- singular value of on spatial layer

- subset of subcarriers allocated for user in cell

- noise power over subcarrier in cell



xx

LIST OF APPENDIX

APPENDIX TITLE PAGE

A List of Publications 143

B Simulator Code 145

CHAPTER 1

INTRODUCTION

1.1 Overview

The Third Generation Partnership Project (3GPP) Long Term Evolution

(LTE)-Advanced is envisaged as the fourth generation cellular standard, and is

aligned with existing third generation deployments, e.g., Universal Mobile

Telecommunications System (UMTS). The goals of LTE-Advanced are to improve

the peak throughput by increasing the numbers of transmit and receive antennas. One

of the key enabling technologies of LTE-Advanced is coordinated multipoint (CoMP)

that targets to improve the cell-edge performance as well as overall network spectral

efficiency through base stations (BSs) coordination.

The continually increasing number of users and the rise of resource-

demanding services require a higher link rate. Due to the limited resources at the

base station (BS) such as bandwidth and power, intelligent allocation of these

resources is crucial for delivering the best possible quality of service (QoS) to the

consumer with the least cost. This is especially important with the high data rates

envisioned for the future wireless standards.

In this thesis, resource allocation algorithm for CoMP LTE-Advanced

network that provides high QoS while is proposed. The proposed algorithm takes

advantage of frequency, spatial and time diversities in the time-varying wireless

channel to increase the CoMP network performance.

2

1.2 Problem Statement

In wireless communication systems, two major detrimental effects that

degrade network performance are the channel’s time-varying nature and interference

[1], [2]. Because of effects such as multipath fading, shadowing and path loss, the

signal to interference plus noise ratio (SINR) at a receiver output can fluctuate [3],

[4]. The other major challenge for the system design is the limited resources such as

bandwidth and power. Frequency reuse is a common method used to improve the

wireless system capacity [5]–[8]. However, it causes interference to users located at

the cell-edge area known as inter-cell interference (ICI) that degrades the link

quality. Since different users have different channels and locations at different times,

resource allocation should be designed to take advantage of the time, frequency and

spatial diversities [9], [10].

One of the tasks carried out by BS in LTE-Advanced network is to manage

network resources for uplink and downlink transmissions to meet the expectation of

the users. It should take into account the quality of service (QoS) requirements of the

respective users’ applications. Moreover, coordinated multipoint (CoMP) allows BS

coordination through backhaul link such that interference generated to neighboring

cells can be minimized [1], [11]–[16]. However, there exists a performance and

signaling overhead tradeoff that needs to be considered. Therefore, different

cooperation levels should be applied in CoMP network in order to reduce the amount

of signaling over the backhaul link [17]–[22] .

Resource allocation problems in wireless network are often solved using

either suboptimal or optimal approaches [9], [10], [23]. The suboptimal resource

allocation schemes are formulated based on less complexity algorithms which lead to

lower computational time. Optimized resource allocation schemes on the other hand,

are typically solved using more complex algorithms that require higher computing

time. In general, the optimal solutions yields better performance results compared to

the suboptimal solutions. Hence, trade-off between performance and computational

complexity is a major factor that should be considered in solving resource allocation

problems. The requirements on complexity must be realistic for practical

implementation.

3

To optimize the system’s overall performance, the resource allocation

schemes often allocate most of the radio resources to the users with good channel

conditions [24]–[32]. However, this allocation can be very unfair, because the users

with poor channel conditions will not have the chance to get the resources at all,

although the users in the same class may pay the same cost for their services. For

instance, allowing users with good channel quality to transmit may result in high

throughput, but meanwhile sacrifice the transmissions of other users. Therefore, there

is a need to trade-off between system performances and fairness among users.

Moreover, it is beneficial to incorporate the upper-layer (e.g., MAC layer)

resource allocation together with the physical layer to exploit various diversities of

wireless channel [33]–[37]. The approach is known as cross-layer design (CLD). The

conventional single-layer design approach at physical layer and MAC layer for

example, fails to exploit the dynamic nature of the physical layer and is suboptimal

in multiuser wireless channels. This motivates the need for CLD in order to achieve

good system-level performance.

1.3 Research Objectives

The main goal of the work is to develop resource allocation strategies for

CoMP LTE-Advanced network that provides high QoS. The resource allocation

approach should be able to take advantage of diversities offered in multiuser wireless

networks, specifically in frequency, spatial and time domains. In order to achieve the

main goal of the work, the specific objectives of the research include:

i) To develop low-complexity resource allocation algorithm that can

achieve high throughput.

ii) To develop optimized resource allocation algorithm that can improve

network throughput while ensuring fairness.

4

iii) To include cross-layer design (CLD) in the proposed optimized

resource allocation algorithm to further enhance network performance.

The low-complexity resource allocation algorithm is assumed suboptimal

since it gives reasonable network performance using simple algorithm. The resource

allocation takes advantage of frequency, spatial and time diversities to benefit

additional network improvement. The optimized resource allocation algorithm tries

to achieve high network throughput while ensuring fair allocation among users. The

CLD approach employs prioritize scheduling in the optimized resource allocation.

1.4 Scope of Research

The proposed algorithm allocates system bandwidth and power among users

in multi-antenna CoMP LTE-Advanced network. The work exploits diversities in

different domains, specifically in frequency, spatial and time available at physical

layer and MAC layer. Furthermore, cooperative communications such as CoMP

efficiently take advantage of the broadcasting nature of wireless networks. The basic

assumption is that BSs in CoMP network share useful information such as CSIs and

data streams, form a virtual antenna array thus providing diversity that can

significantly improve system performance.

In addition, the proposed resource allocation algorithm is optimized to select

a set of users to be scheduled together with optimal subcarrier and power allocation.

The ORA algorithm tries to achieve its desired performance goal by considering two

practical constraints; the available BS power and the individual user minimum rate

requirement.

Besides, CLD scheduling approach is used to exploit time diversity in the

time-varying wireless channels. In this research work, the proportional fair

scheduling scheme is employed at the MAC layer to ensure unbiased allocation of

frequency-time resources among participating users.

5

The performance of the proposed resource allocation algorithm is carried out

through simulation using MATLAB simulation environment. The network model

and the proposed resource allocation algorithm is developed and evaluated through

mathematical analysis.

1.5 Significance of Research

In wireless systems, interference is a major factor that limits the total network

capacity. In this work, the allocation of system bandwidth and power among users in

the network are coordinated such that the interference generated to other cells is

minimized. This is also known as inter-cell interference coordination (ICIC), which

is able to increase the overall network throughput.

The proposed optimized resource allocation that exploits CLD approach can

also be adopted in multi-cellular network such as multi-tier mesh WiFi network.

However, the mesh nodes (base stations) should be able to coordinate among

themselves. The proposed algorithm is also applicable in highly dense populated

cellular network as it is able to achieve high throughput performance while ensuring

fairness.

1.6 Research Contribution

In this research, a resource allocation strategy for CoMP LTE-Advanced

network has been proposed. The proposed algorithms allocate system bandwidth and

transmission power to the users in the network. The algorithm provides high QoS in

the downlink transmissions by exploiting diversities available at the physical layer

and MAC layer. The research contributions provided in this thesis includes:

6

i. Low-complexity Resource Allocation (LRA) algorithm

The initial resource allocation algorithm, LRA has been formulated for total

network throughput maximization. The Frobenius norm of users’ channels is

adopted as the utility function because it reflects the total channel gain. LRA

consists of three modules; user selection module, subcarrier allocation

module and power allocation module. These modules are performed

sequentially to reduce computational complexity. LRA is a simple algorithm

suitable for practical system implementation.

ii. Optimized Resource Allocation (ORA) algorithm

The ORA has been proposed to improve throughput performance in LRA

while ensuring fairness. The ORA algorithm is formulated to maximize the

network proportional fairness utility under practical constraints. The

algorithm optimally allocates available bandwidth and power among

participating users in the network. The Lagrangian method is used to find the

closed-form solution for the constrained optimization problem.

iii. Cross-Layer Design of ORA (CLD-ORA)

The ORA algorithm has been further enhanced to CLD-ORA approach. The

approach combines parameter from MAC layer together with the proposed

ORA algorithm to exploit time diversity through scheduling. The scheduling

algorithm allocates the time resources (e.g., resource blocks) among active

users in the network based on proportional fair scheduling.

LRA can be used for network operator that requires low complexity scheme,

however at the expense of lower throughput and fairness problem. On the other hand,

ORA can be selected to achieve good throughput and fairness tradeoff but it is more

complex. CLD-ORA can be adopted to gain further throughput enhancement,

however at the expense of higher complexity due to MAC scheduling.

7

1.7 Thesis Organization

This thesis consists of seven chapters which cover the three main

contributions of the research. The background, problem statement, objectives, scopes

and contributions of the research are presented in Chapter 1.

Chapter 2 highlights the literature review on CoMP technology, resource

allocation strategy, scheduling scheme, optimization techniques, cross-layer design

and the related work to this research. This chapter also presented several key design

issues and related works in bandwidth allocation and power allocation for LTE-

Advanced network. Additionally, current resource allocation approaches addressing

the issues of throughput performance and fairness among users for CoMP LTE-

Advanced network have also been analyzed and several research gaps have been

identified to become the niche for this research work.

Chapter 3 primarily emphasizes on the design architecture of the proposed

resource allocation algorithm. It covers the basic design concept and the network

model. The algorithm of the proposed resource allocation algorithm is described in

detail using flowchart, block diagram and pseudocode for ease of understanding. In

addition, it also includes the simulation platform, parameter configurations and the

performance metrics used.

Chapter 4 presents the detail on the first contribution which is the initial

resource allocation algorithm with low-complexity, LRA. This includes the design

approach of the proposed resource allocation scheme. More importantly, this chapter

also provides the simulation study and performance analysis of LRA in comparison

to other low-complexity algorithm.

Chapter 5 describes the detail on second contribution which is ORA. It covers

the optimization framework and formulation. The optimization is based on

Lagrangian method. Then, the chapter evaluates its performance in terms of network

8

sum-rate, average transmit power, fairness, spectrum efficiency and energy

efficiency.

Chapter 6 presents the third contribution which is CLD-ORA. In this chapter,

the framework of the proposed resource allocation scheme is presented. Basically,

CLD-ORA is an advancement of ORA presented in the previous chapter. Scheduling

algorithm employed at MAC layer has the responsibility for allocating time resources

among users. Simulation study and performance analysis of CLD-ORA are also

provided in this chapter.

Finally, Chapter 7 concludes the thesis with summary of the research work,

along with recommendations for future work.

9

CHAPTER 2

BACKGROUND AND RELATED WORKS

2.1 Overview

In order to ensure efficient resource allocation in CoMP network, there is a

need to take advantage of frequency, spatial and time diversities. Many research

works on resource allocation in CoMP focused on one or combinations of any two of

the diversities stated above. Previous research efforts in [38]–[40] only assumed

single-antenna BSs, hence the spatial diversity is not being exploited when the

resource allocation is performed. On the other hand, the resource allocation approach

in [41] only considered frequency and spatial diversities, while [42] assumed

frequency and time diversity in the study. Hence, in order to provide more efficient

resource allocation, there is a need to exploit frequency, spatial and time diversities.

The research aim is concerning on resource allocation strategies that can

ensure high QoS in CoMP LTE-Advanced networks. Existing research work related

to CoMP technology in LTE-Advanced networks, optimization techniques and the

resource allocation schemes are provided in this chapter. Section 2.2 describes the

important aspects of CoMP LTE-Advanced networks. Some research challenges

related to CoMP are presented in Section 2.3 and Section 2.4 emphasizes on

optimization method. Some related works that close to the proposed approach are

reviewed in Section 2.6. Finally, the research motivation is presented in Section 2.7.

10

2.2 Coordinated Multipoint (CoMP) in LTE-Advanced

2.2.1 LTE-Advanced

The LTE Release 8 targets to provide a high data rate, reduced latency and

seamless integration with existing systems. In 2008, further requirement

advancements of the LTE Release 8 were approved to permit LTE to be accepted as

a radio technology for International Mobile Telecommunications-Advanced (IMT-

Advanced), known as LTE-Advanced. A representative list of LTE-Advanced

requirements for the radio access network is given in Table 2.1 [43]–[48].

Table 2.1 LTE-Advanced system attributes

Feature Requirements

Peak data rate

Peak spectral efficiency

Average cell spectrum efficiency

Cell-edge user spectral efficiency

C-plane latency

U-plane latency

Downlink - 1 Gbps

Uplink - 500 Mbps

Downlink – 30 bps/Hz

Uplink – 15 bps/Hz

Downlink – 3.7 bps/Hz

Uplink – 2.0 bps/Hz

Downlink – 0.12 bps/Hz

Uplink – 0.07 bps/Hz

50 ms from camped to active state

10 ms from dormant to active state

Reduced compared with Release 8

To accomplish these LTE-Advanced requirements, several principle enabling

technologies have been included in LTE-Advanced as presented in Figure 2.1. The

enabling technologies are carrier aggregation, enhanced downlink spatial

multiplexing, relay deployment, CoMP transmission and support for heterogeneous

networks. Carrier aggregation allows several carriers to be aggregated either

contiguously or non-contiguously to provide bandwidth extension. This provides a

significant increase in the peak data rate, allows efficient interference management

11

and supports heterogeneous deployment. Enhanced downlink spatial multiplexing

extends the number of simultaneous data streams from four to eight. Relaying

technique extends the network coverage area, hence improving the cell-edge

performance. On the other hand, CoMP enhances the cell-edge data rates as well as

the overall network data rates through base station (BS) coordination. Heterogeneous

networks comprise of a traditional macrocell-based network augmented with various

types of low-power nodes that address the capacity and coverage challenges resulting

from the growth of data services.

Key enabling technologies in LTE-Advanced

Carrier Aggregation Enhanced MIMO Relaying

Coordinated Multipoint

(CoMP)

Heterogeneous networks

Figure 2.1 The principle enabling technologies in LTE-Advanced

12

2.2.2 CoMP Transmission

The capacity of a MIMO system employing transmit and receive

antennas can be improved by the factor of min . Interestingly, it can be

realized without using extra transmission power or bandwidth. MIMO systems have

been actively studied and successfully implemented for the emerging broadband

wireless access networks [3], [49]–[56]. Two types of downlink MIMO transmission

techniques are supported; diversity techniques and spatial-multiplexing techniques

[3]. The diversity techniques intend to improve the transmission reliability by

transmitting the same data stream from multiple antennas. Meanwhile, spatial

multiplexing can be used to support multi-user MIMO, whereby multiple data stream

are simultaneously transmitted to different users using the same time-frequency

resource [3]. The conceptual diagram of a MIMO system is presented in Figure 2.2.

Transmitter Receiver

1

2

nT

1

2

nR

h11

h21

hnR1

hnRnT

Figure 2.2 An MIMO system

Given that is the channel gain from transmit antenna to the receive

antenna , the channel for the MIMO system, is written as [3]:

13

(2-1)

Among the key challenges encountered by wireless communication systems

designers are fading and interference. Fading limits the coverage and reliability of

any point-to-point wireless connection, meanwhile interference restricts the

reusability of the resource [1], [4], [11], [16], [54]. The conventional multi-user

MIMO forms a MIMO interference channel. Its spatial degrees of freedom are

defined by the number of transmit antennas at each BS. If several BSs form a

cooperating cluster, the network can have additional spatial degrees of freedom. This

concept known as cooperative MIMO, network MIMO, virtual MIMO or CoMP

which offer substantial performance gain and becomes an attractive research topic in

wireless communication.

Recently, CoMP technology has been proposed in 3GPP LTE-Advanced as a

promising way to boost the system spectrum efficiency and the cell-edge

performance. Downlink CoMP implies dynamic coordination among multiple

geographically separated transmission points or base stations (BSs) [57]. The

backhaul link allows the exchange of information which is used to coordinate the BS

transmissions such that interference generated to neighboring cells is minimized. In

other word, the backhaul link is used for radio resource management (RRM)

purposes including ICIC [58]. Two schemes are adopted in downlink CoMP

transmission; joint processing (JP) and coordinated scheduling/ beamforming

(CS/CB).

a) Joint Processing (JP)

JP is classified into joint transmission (JT) and dynamic cell selection (DCS).

Figure 2.3 shows the operating principle of CoMP (JT) in the downlink. In the CoMP

(JT) transmission scheme, multiple BSs in the network cooperatively transmit signals

to a user-end terminal (UE). The main idea of this class of transmission scheme is

either to improve the received signal quality or actively cancel the interference to

14

other UEs, or both [59]. Data intended for a UE is shared and jointly transmitted

from all BSs in the CoMP network and the received signals at the UE will be

coherently or non-coherently added up together [57].

Cell 1

Cell 2

Cell 3

UE1Backhaulnetwork

Figure 2.3 Downlink CoMP (JT) transmission

Further explanation on CoMP (JT) transmission scheme can be described as

follows. As illustrated in Figure 2.3, assume that UE 1 is receiving signals from Cell

1 , Cell 2( ) and Cell 3 ( ). It is also assumed that is the channel gain from

to UE 1. Therefore, the received signal at UE 1, can be expressed as [59]:

(2-2)

where is the signal transmitted at , is the precoding matrix at realized by

some precoding schemes [60]–[70] and is the white noise at the receiver. The best

precoding matrixes for ICIC are selected together with the individual selection of the

best precoding matrix to maximize the SINR. If each cell is serving to its own UEs,

the the SINR for UE 1 can be expressed as [59]:

15

(2-3)

where is the noise power. By applying CoMP (JT) transmission scheme, UE 1 now

is being concurrently served by , and and the received signal at UE 1 is

given by [59]:

(2-4)

The SINR of UE 1 is given as [59]:

(2-5)

Based on equation (2-5), it can be concluded that is set as the

upper-bound of and CoMP (JT) will constantly give a SINR improvement

compared to single-cell transmission.

Figure 2.4 shows the transmission of DCS. In DCS, user data is shared across

the cells in the CoMP network. However, the transmission of data packet is

accomplished by the cell that transmits with the least path loss chosen at the central

BS [58]. Meanwhile, the other cells among the coordinated cells are not transmitting.

Consequently, the received signal power is maximized due to the absence of

interference received from other cells. In addition, the backhaul network

requirements could also be reduced greatly due to lesser amount of signaling.

16

Cell 1

Cell 2

Cell 3

UE1Backhaulnetwork Fast

selection

Not scheduled

Figure 2.4 Downlink CoMP (DCS) transmission

b) Coordinated Scheduling/Beamforming (CS/CB)

In CoMP (CS/CB), data packet transmission for a UE is executed from one

BS in the CoMP network. However, user scheduling/beamforming decisions are

coordinated among the BSs involve in the network. Transmit beamforming weight

for each UE reduces the undesirable interference to other UE. As a result, the cell-

edge performance can be improved. A number of research studies have been

conducted in this area [71]–[76]. The operating principle of CoMP (CS/CB)

transmission scheme is shown in Figure 2.5.

17

Cell 1

Cell 2

Cell 3

UE1

Interference coordinaation beamforming

Backhaul network

Figure 2.5 Downlink CoMP (CS/CB) transmission

2.2.3 Multicarrier Modulation and Multiple Access

The fundamental idea of OFDM is to split the system spectrum into multiple

narrowband channels, known as subcarriers. The time-domain waveforms of the

subcarriers are orthogonal, but their spectrums overlap in frequency domain [3], as

illustrated in Figure 2.6. Hence, efficient spectrum utilization is realized. Among the

principle advantages of OFDM systems are good performance in frequency selective

fading channels, good spectral property, low receiver complexity, and link adaptation.

18

Figure 2.6 Spectral characteristic of OFDM transmission scheme [3]

Orthogonal frequency division multiple access (OFDMA) has been approved

as the downlink access technology of LTE and LTE-Advanced. OFDMA is a

multiple access technique that allows several UEs to select their own subset of

subcarriers with better channel quality, which results in improved bandwidth

efficiency. This is also known as multi-user diversity gain [3]. Although OFDMA

has good immunity towards intra-cell interference, it encounters the ICI problem due

to the reuse of time and frequency resources in adjacent cells.

19

2.2.4 Downlink Radio Resources

A radioframe consists of 10 subframes, each with 1 ms duration. Each

subframe is made up of two 0.5 ms consecutive slots. Each slot is further spit into

OFDM symbols for the downlink transmission. It is assumed that each slot is

made up of seven OFDM symbols and resource element is one subcarrier in a single

OFDM symbol. A resource block (RB) is expressed as consecutive subcarriers

in the frequency domain and OFDM symbols in the downlink. Thus, a RB is

consists of resource elements, with 180 kHz frequency span and one

slot in time span. For the case of 15 kHz subcarrier spacing, is set to 12 [77],

[78].

One of the fundamental technologies in LTE is the multiple antennas (i.e.,

MIMO) operation. The reference symbols enable the receiver to separate different

antennas from each other. In order to prevent the corruption of channel estimation

needed for MIMO stream separation, each reference symbol should be used by a

single transmit antenna only, as illustrated in Figure 2.7. The reference symbols and

empty resource elements are mapped to alternate between antennas. For the case of

four antenna ports, the corresponding reference signals are denoted as and

.

20

0 1 2 18 19………………………………..3

Radio frame = 10 ms

Slot = 0.5 ms Subframe = 1.0 ms

.

.

.

.

.

.

.

.

.

.

.

.

1 R

eso

urc

e B

lock

(R

B)

con

tain

ing

NR

B

sub

carr

iers

Resource Element

Figure 2.7 LTE downlink frame structure

21

E1 E3 E0 E1 E3 E0

E0 E2 E1 E0 E2 E1

E1 E2 E0 E1 E2 E0

E0 E3 E1 E0 E3 E1

E1 E2 E0 E1 E2 E0 E1 E3 E0 E1 E3 E0

E0 E3 E1 E0 E3 E1 E0 E2 E1 E0 E2 E1

11

10

9

8

7

6

5

4

3

2

1

0

1 R

B =

12

su

bca

rrie

rs

0 1 2 3 4 5 6 0 1 2 3 4 5 6

Even-numbered slots Odd-numbered slots

1 subframe = 1.0 ms

Figure 2.8 OFDMA reference symbols to support MIMO transmission

2.3 Challenges of CoMP

2.3.1 Clustering

Although CoMP has the capability to notably enhance spectrum efficiency

and cell-edge performance, it requires signaling overhead for the purpose of

information exchange over the backhaul link. Therefore, for practical system

implementation, a restricted number of BSs can cooperate to keep the overhead

tolerable. The issue of which BSs should cooperate in order to benefit CoMP

efficiently has attracted a lot of attention [79]–[84].

22

In general, the issue of clustering is approached from two different

perspectives; static clustering and dynamic clustering. Static clusters are designed

based on geographical criteria and kept constant over time. For instance in [84], it

was shown that both in a hexagonal cell layout and under a realistic deployment,

static clustering performs close to UE-specific clustering, while requiring negligible

signaling overhead. Meanwhile, in the case of dynamic clustering, the system

continuously adapts the clustering strategy to varying parameters such as UE

locations and radio frequency (RF) conditions. Several dynamic clustering schemes

that offer significant advancement are proposed in [79]–[81], [83]. The authors of

[82] show that dynamically-clustered cooperative networks outperform both

statically-clustered cooperative networks.

2.3.2 Synchonization

Another major challenge related to CoMP is the synchronization of

cooperating served devices in time and frequency. Precise synchronization facilitates

scheduling of communication resources and interference avoidance in multi-access

networks. On one hand, there are different local oscillators in each BS and mobile

terminal that lead to deviations in the carrier frequency according to its nominal

value. On the other hand, there are variations in the symbol timing between each

transmitter and receiver station. Both effects need to be compensated by

synchronization techniques. These techniques can be used separately or in

conjunction to facilitate the establishment of a common notion of frequency and time

to a desired level of accuracy. Two classes of synchronization techniques are mostly

applied; network synchronization techniques and satellite-based synchronization

techniques [14]. Some studies related to these synchronization techniques can be

found in [85], [86].

23

2.3.3 Channel Knowledge

Accurate channel estimation is the basis for any CoMP transmission scheme.

Channel estimation affects precoding accuracy and defines an upper bound for

possible performance gains. Specifically, CoMP (JP) faces several further challenges

compared to conventional single link systems such as a high number of channel

components to be estimated, strong multi-cell interference due to frequency reuse

one and a high sensitivity of joint precoding with respect to estimation errors

combined with the need for frequency-selective channel information [14].

Two dimensional Wiener filtering and subspace concept are two well-known

channel estimation techniques [87]–[91]. The effective channel concept is presented

which restricts the number of effective channel components to be estimated to the

number of supported data streams. In 3GPP, similar concepts are discussed under the

label of implicit versus explicit channel estimation and feedback [55], [92], [93]. The

latter is more or less the direct feedback of the quantized channel components, while

implicit includes pre- or postcoders like beamformers from an LTE Release 8

codebook.

2.3.4 Backhaul

Most BS cooperation schemes require information exchange over a backhaul

infrastructure [17]–[20], [94]. Depending on the existing infrastructure of a mobile

operator, both backhaul capacity and latency requirements of some CoMP schemes

may be the principle cost drivers on the roadmap towards CoMP. In general, the

CoMP network size is a main backhaul driver for most of the schemes, being another

reason why CoMP should only be performed in reasonably dimensioned clusters. In

both uplink and downlink, the backhaul link is required for the exchange of user data,

CSI and scheduling decisions.

24

The extent of these different types of backhaul data depends on the particular

CoMP transmission scheme used and the chosen network topology. For instance, an

inter-site BS cooperation can be considered as the worst case scenario, since a single

backhaul link must support all incoming and outgoing backhaul traffic generated by

BS cooperation. Meanwhile, if BS cooperation involves BSs located at the same site

(intra-site BS cooperation), the information exchanged between cooperating BSs are

not delivered through backhaul. When selecting a CoMP scheme, not only the related

backhaul requirements must be considered, but also the achievable enhancement in

spectrum efficiency and cell-edge performance.

2.4 Optimization Methods

Optimization methods are extensively applied in many areas such as

engineering, economics, management, physical sciences, etc. The ultimate role of

optimization methods is to select the best or a satisfactory one from amongst the

possible solutions to an optimization problem [95]. The process of using

optimization methods principally involves the formulation of the optimization

problem and the selection of appropriate numerical method.

A metaheuristic method is an iterative generation process which combines

different concepts for exploring (global search) and exploiting (local search) the

search space [95]. Learning strategies are used to construct information to search

near-optimal solutions. Examples of metaheuristic methods include evolutionary

algorithms, tabu search, differential evolution and swarm intelligence.

2.4.1 Swarm Intelligence

Swarm intelligence algorithm emphases on the collective behaviors that result

from the local interactions of the individuals with each other and with the

129

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